Proceedings of the 2021 7th International Conference on Computing and Data Engineering最新文献_第2页

Spatio-Temporal Multi-Scale Convolutional Network for Traffic Forecasting 交通预测的时空多尺度卷积网络

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456210

Qidong Liu, Tong Li, Rui Zhu, Zhen Hou, Zehui Zhang, Maoyu Chen, Bing Yang

{"title":"Spatio-Temporal Multi-Scale Convolutional Network for Traffic Forecasting","authors":"Qidong Liu, Tong Li, Rui Zhu, Zhen Hou, Zehui Zhang, Maoyu Chen, Bing Yang","doi":"10.1145/3456172.3456210","DOIUrl":"https://doi.org/10.1145/3456172.3456210","url":null,"abstract":"Effective traffic forecasting contributes to the public safety and urban management, and the complexity and variability of the traffic system make it difficult to predict traffic over a long-term horizon. In this paper, we focus on analyzing the spatio-temporal features of traffic, and propose a spatio-temporal multi-scale convolutional net-work (ST-MSCN) to solve the problem of traffic flow prediction. First, in order to directly capture the spatial dependence and multi-scale features of urban traffic flow, we propose an MSC unit. In addition, this paper also proposes a multi-level feature fusion strategy to combine low-level surface features and high-level abstract features to effectively avoid feature loss. Finally, we propose an early fusion mechanism and combine it with the MSC unit to ensure the improved prediction results while greatly reducing the complexity of the model. As for the simulation, Beijing taxi trajectory data and New York City bicycle trajectory data are used to carry out the simulation experiments. The experimental results show the advanced nature of our model, and prediction accuracy are 7.58% ∼ 9.23% higher than state-of-the-art.","PeriodicalId":133908,"journal":{"name":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","volume":"86 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120904101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization Techniques in Data Management: A Survey 数据管理中的优化技术:综述

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456214

Edjola Naka, V. Guliashki

{"title":"Optimization Techniques in Data Management: A Survey","authors":"Edjola Naka, V. Guliashki","doi":"10.1145/3456172.3456214","DOIUrl":"https://doi.org/10.1145/3456172.3456214","url":null,"abstract":"Data Management can be defined as the process of extracting, storing, organizing, and maintaining the data created and collected in organizations. Today's organizations invest in data management solutions that provide an efficient way to manage data in a unified structure. The enormously growth of data in the last decades has created a necessity for the fast extracting, accessing, and processing of the data. Optimization has been a key component in improving the system's performance, searching and accessing data in different data management solutions. Optimization is a mathematical discipline that formulates mathematical models and finds the best solution among a set of feasible solutions. This paper aims to give a general overview of applications of optimization techniques and algorithms in different areas of data management in the last decades. Data management includes a large group of functionalities, but we will focus on studying and reviewing the recent development of optimization algorithms used in databases, data warehouses, big data and machine learning. Furthermore, this paper will identify applications of optimization in data management, reviews the current solutions proposed and emphasize future topics where there is a lack of studies in data management.","PeriodicalId":133908,"journal":{"name":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129832313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved KNN Algorithm based on Probability and Adaptive K Value 基于概率和自适应K值的改进KNN算法

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456201

Yulong Ling, Xiao Zhang, Yong Zhang

引用次数: 1

A Tourism Knowledge Model through Topic Modeling from Online Reviews 基于在线评论主题建模的旅游知识模型

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456211

Valentinus Roby Hananto, U. Serdült, V. Kryssanov

{"title":"A Tourism Knowledge Model through Topic Modeling from Online Reviews","authors":"Valentinus Roby Hananto, U. Serdült, V. Kryssanov","doi":"10.1145/3456172.3456211","DOIUrl":"https://doi.org/10.1145/3456172.3456211","url":null,"abstract":"Ontologies and knowledge models have gained more recognition because of their extensive use in recommender systems. The lack of automatic approaches in ontology engineering, however, becomes a challenge to fulfill increasing needs for such knowledge models in the field of tourism. In this study, a system for building tourism knowledge models from online reviews is proposed. The main contribution of the study is the application of topic modeling to build a knowledge model that, in turn, allows for an automated labeling process to train classifiers. Given a collection of unlabeled tourism online reviews, Latent Dirichlet Allocation (LDA) is applied to automatically label each document. Each topic discovered by LDA is labeled with one specific category, representing its semantic meaning based on an existing general ontology as a reference. These automatically labeled documents are used for classification, and the result is compared with manual annotation. Experiments on Indonesian tourism datasets showed that the automatic labeling approach using LDA provides for a precision score of 70%. In classification tasks, this approach can achieve comparable or even better classification performance than the manual labeling. The results obtained suggest that the developed system is capable of building a tourism knowledge model and providing acceptable-quality training data for the development of tourism recommender systems.","PeriodicalId":133908,"journal":{"name":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126146588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Proceedings of the 2021 7th International Conference on Computing and Data Engineering 2021第七届计算与数据工程国际会议论文集

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172

引用次数: 1

Near-Native Interrupt Latency in Real-Time Guests: Handler Emulation Through Memory Map Morphing 实时来宾的近本地中断延迟:通过内存映射变形的处理程序仿真

Proceedings of the 2021 7th International Conference on Computing and Data Engineering Pub Date : 2021-01-15 DOI: 10.1145/3456172.3456197

Farhad Andalibi, Paulo Garcia

{"title":"Near-Native Interrupt Latency in Real-Time Guests: Handler Emulation Through Memory Map Morphing","authors":"Farhad Andalibi, Paulo Garcia","doi":"10.1145/3456172.3456197","DOIUrl":"https://doi.org/10.1145/3456172.3456197","url":null,"abstract":"Interrupt latency, critical in real-time applications, is increased in virtualized systems; interrupts intended for a non-running partition can be delayed up to the length of the scheduling period. State-of-the art techniques for decreasing latency in real-time guests relax temporal isolation requirements, allowing guests’ interrupt handlers to be executed within another partition’s time slot. However, this approach does not yet achieve native latencies, due to the need to switch guests’ contexts, inducing overhead for Hypervisor execution. This paper presents an approach for reducing interrupt latencies: by taking advantage of virtualization support hardware in modern microprocessors, we show that real-time guests’ interrupt handlers can be executed within Hypervisor hardware context, i.e., by Hypervisor-dedicated processor hardware, eliminating the need for switching guests’ context and approaching native latencies. This is achieved by morphing the Hypervisor’s memory map translation mechanisms, so software is executed within the real-time guest’s memory context, allowing near-native interrupt latency. We evaluate our implementation, consisting of virtualization hardware and software, on a softcore ARM processor prototyped on a Xilinx Virtex 5 FPGA, running Linux and FreeRTOS. Results show memory map morphing is capable of achieving near-native interrupt latency for a real-time guest, outperforming the state of the art.","PeriodicalId":133908,"journal":{"name":"Proceedings of the 2021 7th International Conference on Computing and Data Engineering","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120957601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1